TY - JOUR
T1 - GeoFINDR
T2 - Practical approach to verify cloud instances geolocation in multicloud
AU - Ider, Saïd
AU - Laurent, Maryline
N1 - Publisher Copyright:
© 2025 The Author(s)
PY - 2026/2/1
Y1 - 2026/2/1
N2 - In multicloud environments, where legal obligations, technical constraints and economic interests are at stake, it is of interest for stakeholders to be able to locate cloud data or the cloud instance where data are decrypted for processing. This paper proposes an original and practical delay-based approach, called GeoFINDR, to locate a cloud instance, e.g. a Virtual Machine (VM), over the Internet, based on RIPE Atlas landmarks. First, the assumed threat model and assumptions are more modern than in existing solutions, e.g. VM-scale localization in multicloud environments, a Cloud Service Provider (CSP) lying over the location of the VM. Second, the originality of the approach lies in four original ideas: (1) geolocation is performed from the VM, (2) a Greedy algorithm selects a first set LMA of distributed audit landmarks in the vicinity of the declared area, (3) a sectorization algorithm identifies a set LMS of other landmarks with distance-delay behavior similar to that of the VM to estimate the sector of the VM, and (4) the estimated location of the VM is calculated as the barycentre position of the LMS landmarks. An open source tool is published on GitHub and experiments show that the localization accuracy can be as high as 22.1km, under adverse conditions, where the CSP lies about the location of the VM.
AB - In multicloud environments, where legal obligations, technical constraints and economic interests are at stake, it is of interest for stakeholders to be able to locate cloud data or the cloud instance where data are decrypted for processing. This paper proposes an original and practical delay-based approach, called GeoFINDR, to locate a cloud instance, e.g. a Virtual Machine (VM), over the Internet, based on RIPE Atlas landmarks. First, the assumed threat model and assumptions are more modern than in existing solutions, e.g. VM-scale localization in multicloud environments, a Cloud Service Provider (CSP) lying over the location of the VM. Second, the originality of the approach lies in four original ideas: (1) geolocation is performed from the VM, (2) a Greedy algorithm selects a first set LMA of distributed audit landmarks in the vicinity of the declared area, (3) a sectorization algorithm identifies a set LMS of other landmarks with distance-delay behavior similar to that of the VM to estimate the sector of the VM, and (4) the estimated location of the VM is calculated as the barycentre position of the LMS landmarks. An open source tool is published on GitHub and experiments show that the localization accuracy can be as high as 22.1km, under adverse conditions, where the CSP lies about the location of the VM.
KW - Delay-based geolocation
KW - Dishonest cloud service providers
KW - GeoFINDR
KW - Multicloud
KW - RIPE Atlas
KW - VM-Scale localization
UR - https://www.scopus.com/pages/publications/105023956658
U2 - 10.1016/j.comnet.2025.111862
DO - 10.1016/j.comnet.2025.111862
M3 - Article
AN - SCOPUS:105023956658
SN - 1389-1286
VL - 275
JO - Computer Networks
JF - Computer Networks
M1 - 111862
ER -